• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1809-1816
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• 2021

In order to identify the latest research trends using data related to national R&D projects and to produce and utilize meaningful information, the application of automatic classification technology was also required in the national R&D information service, so we conducted research to automatically classify and recommend research field. About 450,000 cases of national R&D project data from 2013 to 2020 were collected and used for learning and evaluation. A model was selected after data pre-processing, analysis, and performance analysis for valid data among collected data. The performance of Word2vec, GloVe, and fastText was compared for the purpose of deriving the optimal model combination. As a result of the experiment, the accuracy of only the subcategories used as essential items of task information is 90.11%. This model is expected to be applicable to the automatic classification study of other classification systems with a hierarchical structure similar to that of the national science and technology standard classification research field.

• ETRI Journal
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• v.39 no.1
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• pp.87-96
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• 2017

Ultrasonic surgical devices are routinely used for surgical procedures. The incision and coagulation of tissue generate a temperature of $40^{\circ}C-150^{\circ}C$ and depend on the controllable output power level of the surgical device. Recently, research on the classification of grasped tissues to automatically control the power level was published. However, this research did not consider the specific characteristics of the surgical device, tissue denaturalization, and so on. Therefore, this research proposes a robust algorithm that simulates noise to resemble real situations and classifies tissue using conventional classifier algorithms. In this research, the bioimpedance spectrum for six tissues (liver, large intestine, kidney, lung, muscle, and fat) is measured, and five classifier algorithms are used. A signal-to-noise ratio of additive white Gaussian noise diversifies the testing sets, and as a result, each classifier's performance exhibits a difference. The k-nearest neighbors algorithm shows the highest classification rate of 92.09% (p < 0.01) and a standard deviation of 1.92%, which confirms high reproducibility.

• ETRI Journal
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• v.37 no.3
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• pp.541-550
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• 2015

In this paper, we propose a classification-based approach for hybridizing statistical machine translation and rulebased machine translation. Both the training dataset used in the learning of our proposed classifier and our feature extraction method affect the hybridization quality. To create one such training dataset, a previous approach used auto-evaluation metrics to determine from a set of component machine translation (MT) systems which gave the more accurate translation (by a comparative method). Once this had been determined, the most accurate translation was then labelled in such a way so as to indicate the MT system from which it came. In this previous approach, when the metric evaluation scores were low, there existed a high level of uncertainty as to which of the component MT systems was actually producing the better translation. To relax such uncertainty or error in classification, we propose an alternative approach to such labeling; that is, a cut-off method. In our experiments, using the aforementioned cut-off method in our proposed classifier, we managed to achieve a translation accuracy of 81.5% - a 5.0% improvement over existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2927-2941
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• 2022

The primary task of machine fault diagnosis is to judge whether the current state is normal or damaged, so it is a typical binary classification problem with mutual exclusion. Mutually exclusive events and out-of-domain detection have one thing in common: there are two types of data and no intersection. We proposed a fusion model method to improve the accuracy of machine fault diagnosis, which is based on the mutual exclusivity of events and the commonality of out-of-distribution detection, and finally generalized to all binary classification problems. It is reported that the performance of a convolutional neural network (CNN) will decrease as the recognition type increases, so the variational auto-encoder (VAE) is used as the primary model. Two VAE models are used to train the machine's normal and fault sound data. Two reconstruction probabilities will be obtained during the test. The smaller value is transformed into a correction value of another value according to the mutually exclusive characteristics. Finally, the classification result is obtained according to the fusion algorithm. Filtering normal data features from fault data features is proposed, which shields the interference and makes the fault features more prominent. We confirm that good performance improvements have been achieved in the machine fault detection data set, and the results are better than most mainstream models.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.4
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• pp.219-228
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• 2006

Many biomedical research groups have been trying to share their outputs to increase the efficiency of research. As part of their efforts, a common ontology named Gene Ontology(GO), which comprises controlled vocabulary for the functions of genes, was built. However, data from many research groups are distributed and most systems don't support integrated semantic queries on them. Furthermore, the semantics of the associations between concepts from external classification systems and GO are still not clarified, which makes integrated semantic query infeasible. In this paper we present an ontology matching and integration system, called AutoGOA, which first resolves the semantics of the associations between concepts semi-automatically, and then constructs integrated ontology containing concepts from GO and external classification systems. Also we describe a web-based application, named GOGuide II, which allows the user to browse, query and visualize integrated data.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.35-43
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• 2023

Emotion recognition while driving is an essential task to prevent accidents. Furthermore, in the era of autonomous driving, automobiles are the subject of mobility, requiring more emotional communication with drivers, and the emotion recognition market is gradually spreading. Accordingly, in this research plan, the driver's emotions are classified into seven categories using psychological and behavioral data, which are relatively easy to collect. The latent vectors extracted through the auto-encoder model were also used as features in this classification model, confirming that this affected performance improvement. Furthermore, it also confirmed that the performance was improved when using the framework presented in this paper compared to when the existing EEG data were included. Finally, 81% of the driver's emotion classification accuracy and 80% of F1-Score were achieved only through psychological, personal information, and behavioral data.

• Journal of Biomedical Engineering Research
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• v.41 no.2
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• pp.101-106
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• 2020

Although benign gastric ulcers do not develop into gastric cancer, they are similar to early gastric cancer and difficult to distinguish. This may lead to misconsider early gastric cancer as gastric ulcer while diagnosing. Since gastric cancer does not have any special symptoms until discovered, it is important to detect gastric ulcers by early gastroscopy to prevent the gastric cancer. Therefore, we developed a Convolution Neural Network (CNN) model that can be helpful for endoscopy. 3,015 images of gastroscopy of patients undergoing endoscopy at Gachon University Gil Hospital were used in this study. Using ResNet-50, three models were developed to classify normal and gastric ulcers, normal and gastric cancer, and gastric ulcer and gastric cancer. We applied the data augmentation technique to increase the number of training data and examined the effect on accuracy by varying the multiples. The accuracy of each model with the highest performance are as follows. The accuracy of normal and gastric ulcer classification model was 95.11% when the data were increased 15 times, the accuracy of normal and gastric cancer classification model was 98.28% when 15 times increased likewise, and 5 times increased data in gastric ulcer and gastric cancer classification model yielded 87.89%. We will collect additional specific shape of gastric ulcer and cancer data and will apply various image processing techniques for visual enhancement. Models that classify normal and lesion, which showed relatively high accuracy, will be re-learned through optimal parameter search.

• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.293-300
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• 2011

The CGI(Computer-generated Image) techniques provide designers with an effective means of creating design artifacts in an automatic way. It has been pointed that two important activities while applying the CGI techniques are both image generation and managemental issues for the generated images. By applying automatic generation techniques for creation of images, designers can acquire benefits in that they can produce free style results in a simple way. Along with such benefits, it is also important for designer to identify and to establish well defined mechanisms for storing vast quantity of auto-generated CGIs. However, it is problematic to assign key-words and to classify abstract images mainly because they lack an analogy of the real world entities. This paper presents classification scheme for the abstract CGIs by applying classification and description criteria from the viewpoint of both design elements and emotional elements. Effective classification and specification can help designers build and retrieve desired images in an easy way, and make management process more simple and effective.